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Defining trust in artificial intelligence (AI) for regulatory science is crucial. Successful AI adoption hinges on dialogue, adaptability, and trained personnel for 21st-century regulatory challenges.

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Area of Science:

  • Regulatory Science
  • Artificial Intelligence
  • Trustworthy AI

Background:

  • Trust is a critical, yet debated, concept for AI in regulatory science.
  • The selection of AI models with similar performance but different features raises trust issues.
  • The necessity of explainability for AI in science, mirroring testable scientific theories, is questioned.

Purpose of the Study:

  • To explore the definition and implications of trust in AI within regulatory science.
  • To address challenges in selecting and validating AI models for regulatory applications.
  • To discuss the role of explainability in the adoption of AI for regulatory science.

Main Methods:

  • Discussion and consensus-building among regulators at the Global Summit on Regulatory Science (GSRS24).
  • Analysis of AI model characteristics, performance metrics, and feature usage.
  • Exploration of the relationship between AI explainability and scientific testability.

Main Results:

  • Regulators acknowledged the ongoing debate surrounding the definition of trust in AI.
  • The summit highlighted the complexity of choosing between AI models with equivalent performance but differing underlying features.
  • The critical need for explainability in AI for regulatory science was discussed in the context of scientific rigor.

Conclusions:

  • Successful AI adoption in regulatory science necessitates continuous dialogue and adaptability.
  • Developing AI-trained personnel is essential to effectively leverage AI for regulatory responsibilities.
  • Addressing trust and explainability are key to harnessing AI's potential in the evolving 21st-century regulatory landscape.